class_docstr: A Trainer for data parallel LightGBM training.
  
  This Trainer runs the LightGBM training loop in a distributed manner
  using multiple Ray Actors.
  
  If you would like to take advantage of LightGBM's built-in handling
  for features with the categorical data type, consider applying the
  :class:`Categorizer` preprocessor to set the dtypes in the dataset.
  
  .. note::
      ``LightGBMTrainer`` does not modify or otherwise alter the working
      of the LightGBM distributed training algorithm.
      Ray only provides orchestration, data ingest and fault tolerance.
      For more information on LightGBM distributed training, refer to
      `LightGBM documentation <https://lightgbm.readthedocs.io/>`__.
  
  Example:
      .. testcode::
  
          import ray
  
          from ray.train.lightgbm import LightGBMTrainer
          from ray.train import ScalingConfig
  
          train_dataset = ray.data.from_items(
              [{"x": x, "y": x + 1} for x in range(32)])
          trainer = LightGBMTrainer(
              label_column="y",
              params={"objective": "regression"},
              scaling_config=ScalingConfig(num_workers=3),
              datasets={"train": train_dataset}
          )
          result = trainer.fit()
  
      .. testoutput::
          :hide:
  
          ...
  
  Args:
      datasets: The Ray Datasets to use for training and validation. Must include a
          "train" key denoting the training dataset. All non-training datasets will
          be used as separate validation sets, each reporting a separate metric.
      label_column: Name of the label column. A column with this name
          must be present in the training dataset.
      params: LightGBM training parameters passed to ``lightgbm.train()``.
          Refer to `LightGBM documentation <https://lightgbm.readthedocs.io>`_
          for a list of possible parameters.
      dmatrix_params: Dict of ``dataset name:dict of kwargs`` passed to respective
          :class:`xgboost_ray.RayDMatrix` initializations, which in turn are passed
          to ``lightgbm.Dataset`` objects created on each worker. For example, this
          can be used to add sample weights with the ``weight`` parameter.
      num_boost_round: Target number of boosting iterations (trees in the model).
          Note that unlike in ``lightgbm.train``, this is the target number
          of trees, meaning that if you set ``num_boost_round=10`` and pass a model
          that has already been trained for 5 iterations, it will be trained for 5
          iterations more, instead of 10 more.
      scaling_config: Configuration for how to scale data parallel training.
      run_config: Configuration for the execution of the training run.
      resume_from_checkpoint: A checkpoint to resume training from.
      metadata: Dict that should be made available in `checkpoint.get_metadata()`
          for checkpoints saved from this Trainer. Must be JSON-serializable.
      **train_kwargs: Additional kwargs passed to ``lightgbm.train()`` function.
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code: def preprocess_datasets(self) -> None:
      super().preprocess_datasets()
  
      # XGBoost/LightGBM-Ray requires each dataset to have at least as many
      # blocks as there are workers.
      # This is only applicable for xgboost-ray<0.1.16
      if Version(xgboost_ray.__version__) < Version("0.1.16"):
          self._repartition_datasets_to_match_num_actors()
  
file_path: ray/python/ray/train/lightgbm/lightgbm_trainer.py
imports: import os
  from typing import Any, Dict, Union
  import lightgbm
  import lightgbm_ray
  import xgboost_ray
  from lightgbm_ray.tune import TuneReportCheckpointCallback
  from ray.train import Checkpoint
  from ray.train.gbdt_trainer import GBDTTrainer
  from ray.train.lightgbm import LightGBMCheckpoint
  from ray.util.annotations import PublicAPI
label_desc: Use image node_id 6 for calling the LightGBMTrainer obj's underlying member method code with example usage: obj.preprocess_datasets() without return types